Director AI Data Coordination

Amerihealth Caritas•,

1d•Hybrid

About The Position

The Director, Artificial Intelligence Coordination leads the strategy, product design, and engineering delivery of the enterprise AI platform that powers clinical, operational, and member-facing intelligence across a multistate Medicaid managed care organization. This role blends product management discipline with engineering leadership—owning both the AI product portfolio (vision, roadmap, prioritization, business case) and the engineering organization that builds and operates it. The Director serves as the connective tissue between business strategy, clinical operations, technology, and compliance—translating enterprise priorities into a governed AI roadmap, sequencing investments by ROI and risk, and ensuring every deployed AI capability delivers measurable business outcomes. This is a hands-on leadership role that requires equal fluency in product strategy, stakeholder management, and AI engineering execution. Operating at the intersection of applied AI, healthcare workflows, and regulated product delivery, the Director ensures AI moves from concept to scaled production—reducing administrative burden, accelerating clinical decisioning, improving quality outcomes, and enabling agent-assisted operations across UM, CM, quality, pharmacy, behavioral health, and member engagement.

Requirements

Bachelor's Degree required (Computer Science, Engineering, Data Science, Health Informatics, or related)
Master's Degree strongly preferred (CS, AI/ML, Health Informatics, MBA)
Minimum 5 years of experience delivering AI/ML or data products
3+ years in formal leadership roles (managing managers, senior tech leads, or product managers)
Strong command of product management fundamentals: discovery, road-mapping, prioritization frameworks (RICE, WSJF, MoSCoW), backlog management, Agile/Scrum, and OKRs.
Skilled at writing business cases, value hypotheses, and acceptance criteria that translate strategy into shippable increments.
Working knowledge of agentic AI patterns, LLM orchestration, RAG, and MLOps/LLMOps—sufficient to set architecture direction, evaluate vendors, and challenge engineering tradeoffs.
Familiarity with foundation model providers (Azure OpenAI, Anthropic, open-source), modern data platforms (Databricks, Snowflake), and cloud architecture (Azure preferred).
Understanding of Responsible AI frameworks (NIST AI RMF, ISO/IEC 42001), PHI-safe design patterns, and prompt injection / guardrail concepts.
Healthcare fluency: claims, clinical (EMR/EHR), FHIR/HL7, HEDIS, UM/CM workflows, and HIPAA/PHI handling.
Adept at user research, journey mapping, and human-centered design for AI-enabled workflows.
Demonstrated ability to build and retain elite product and engineering talent; bias toward action balanced with engineering discipline and regulatory pragmatism.
Proven track record building product roadmaps, business cases, and ROI models that secured executive and board-level investment; experience leading vendor selection, Responsible AI governance, and cross-functional executive alignment.

Nice To Haves

Preferred Certifications: CSPO, SAFe POPM, PMP, Azure AI Engineer, or NIST AI RMF practitioner.

Responsibilities

AI Product Strategy & Roadmap: Own the AI product vision, multi-year roadmap, and prioritization framework, aligning investments with enterprise business plan objectives, regulatory timelines, and market growth strategy.
Define and maintain the AI product portfolio—evaluating opportunities by business value, feasibility, risk, and cost-to-serve—and sequence delivery across clinical, operational, and member-facing domains.
Conduct competitive landscape analysis, market scanning, and build-vs-buy assessments to inform product positioning and partner selection across foundation model providers, orchestration platforms, and agentic AI vendors.
Develop business cases, ROI models, and value realization tracking for each AI initiative; present recommendations to executive leadership and governance forums.
Define and track KPIs and OKRs tied to product performance—including adoption, accuracy, cycle time reduction, cost-to-serve, clinical throughput, and user satisfaction.
Product Discovery, Design & Stakeholder Management: Lead product discovery with clinical, operational, compliance, and member experience stakeholders to identify high-ROI use cases and validate problem-solution fit.
Translate business strategy into a scalable product backlog with clearly defined epics, features, and acceptance criteria using Agile/iterative delivery practices.
Apply human-centered design principles to ensure AI products minimize clinician friction, support transparent decision-making, and integrate naturally into existing workflows.
Facilitate governance forums, design reviews, and sprint ceremonies to align delivery with business, clinical, and technology stakeholders.
Communicate complex AI tradeoffs (accuracy vs. cost, automation vs. oversight, speed vs. compliance) clearly to C-suite, board, market presidents, CMOs, and operational leaders.
Engineering Leadership & Team Development: Recruit, build, and lead a multidisciplinary team of AI engineers, ML engineers, platform engineers, and AI product analysts across onshore and partner delivery models.
Establish engineering standards for code review, testing, CI/CD, model and prompt versioning, and production readiness gates—without over-engineering early-stage products.
Develop the AI engineering and product career ladder, defining competency rubrics and mentoring senior engineers, product managers, and tech leads.
Foster a culture that balances rapid experimentation with production discipline—measured by deployed business impact, not prototype volume.
Agentic AI & Workflow Product Delivery: Shape the product strategy for agentic AI workflows that automate clinical and operational tasks—including prior authorization triage, clinical document summarization, appeals drafting, care plan generation, member outreach, and analyst/contact-center copilots.
Partner with clinical and operational product owners to design human-in-the-loop experiences—confidence thresholds, escalation paths, and override capture—ensuring clinical safety and regulatory defensibility.
Define the product release strategy (pilot → controlled rollout → scaled deployment) including success criteria, guardrails, and rollback plans.
Drive adoption through training, change management partnership, and continuous product iteration based on user feedback and performance telemetry.
AI Platform & Production Operations (Engineering Oversight): Set the enterprise AI orchestration reference architecture at a directional level—model gateway, agent framework, retrieval/RAG layer, evaluation harness, and guardrails—and delegate detailed technical design to engineering leads.
Oversee MLOps/LLMOps practices: prompt management, evaluation pipelines, A/B experimentation, drift detection, and continuous improvement workflows.
Ensure production reliability of AI services—uptime SLAs, capacity planning, observability, and incident response—through strong engineering management.
Own the AI unit economics: cost per task, cost per member, cost per resolved case, with active optimization through model right-sizing, caching, and workload routing.
Responsible AI, Governance & Compliance: Co-own (with Compliance, Legal, Medical Affairs, and Information Security) the Responsible AI governance framework—policies, model risk tiers, intake/review workflows, and the enterprise AI registry.
Ensure all production AI products meet HIPAA, CMS, NCQA, state Medicaid, NIST AI RMF, and NAIC Model Bulletin expectations.
Lead product and engineering responses to internal audits, regulator inquiries, and delegated vendor AI oversight reviews.
Champion transparency, explainability, and fairness as core product requirements—not afterthoughts.
Vendor, Partner & Investment Management: Manage strategic AI vendor relationships—foundation model providers, orchestration platforms, agentic AI vendors, and managed services partners.
Negotiate enterprise agreements, monitor consumption, and own the AI investment dashboard reporting spend, value delivered, and forward-looking commitments.
Partner with Procurement, Legal, and Finance to ensure contracts include appropriate data, IP, security, and Responsible AI provisions.